Quantitative multivariate analysis of seizures

ABSTRACT

Methods, including a method, comprising selecting a plurality of dependent variables relating to each of a plurality of seizures in a patient; selecting a plurality of independent variables, wherein each independent variable comprises a therapy parameter, a therapy delivery parameter, a temporal factor, an environmental factor, or a patient factor; quantifying at least one relationship between each of at least two dependent variables and each of at least two independent variables; and performing an action in response to said quantifying, selected from reporting said at least one relationship, assessing an efficacy of a therapy, assessing an adverse effect of said therapy, providing a therapy modification recommendation, or adjusting said therapy. A medical device system capable of implementing a method. A non-transitory computer readable program storage medium containing instructions that, when executed by a computer, perform a method.

This application claims priority to and is a continuation of U.S. patentapplication Ser. No. 13/116,873 filed on May 26, 2011, which claimspriority to U.S. provisional patent application 61/348,674, filed on May26, 2010, the disclosure of each is hereby incorporated by reference inits entirety.

BACKGROUND OF THE DISCLOSURE 1. Field of the Disclosure

The present disclosure relates generally to the field of epilepsy. Moreparticularly, it concerns analysis of seizures to quantify effects ofone or more independent variables on one or more seizure dependentvariables.

SUMMARY OF THE DISCLOSURE

In one embodiment, the present disclosure relates to a medical devicesystem, comprising: a data acquisition unit configured to acquire datarelating to a plurality of dependent variables relating to each of aplurality of seizures in a patient and data relating to a plurality ofindependent variables, wherein each of said independent variablescomprises a therapy parameter, a therapy delivery parameter, a temporalfactor, an environmental factor, or a patient factor; a dataquantification unit configured to quantify at least one relationshipbetween at least two of said dependent variables and at least two ofsaid independent variables; and a responsive action unit configured toperform an action in response to said quantifying, wherein said actionis selected from reporting said at least one relationship, assessing anefficacy of a therapy, assessing an adverse effect of said therapy,providing a therapy modification recommendation, or adjusting saidtherapy.

In one embodiment, the present disclosure relates to a method ofassessing an efficacy of an epilepsy therapy, comprising: selecting aplurality of dependent variables relating to each of a plurality ofseizures in a patient; selecting a plurality of independent variables,wherein each independent variable comprises a therapy parameter, atherapy delivery parameter, a temporal factor, an environmental factor,or a patient factor; quantifying at least one relationship between eachof at least two dependent variables and each of at least two independentvariables; and performing an action in response to said quantifying,selected from reporting said at least one relationship, assessing anefficacy of a therapy, assessing an adverse effect of said therapy,providing a therapy modification recommendation, or adjusting saidtherapy.

In one embodiment, the present disclosure relates to a non-transitorycomputer readable program storage medium containing instructions that,when executed by a computer, perform a method disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, inwhich like reference numerals identify like elements, and in which:

FIG. 1 provides a block diagram of a medical device system, inaccordance with one illustrative embodiment of the present disclosure;

FIG. 2 shows a flowchart of an implementation of a method, in accordancewith one illustrative embodiment of the present disclosure;

FIG. 3 provides a block diagram of a model unit, its inputs, and itsoutputs, in accordance with one illustrative embodiment of the presentdisclosure;

FIGS. 4A-4B provide tabular depictions of a number of exemplaryindependent variables, in accordance with one illustrative embodiment ofthe present disclosure;

FIG. 5 depicts exemplary outputs of a model unit, in accordance with oneillustrative embodiment of the present disclosure;

FIG. 6 depicts another exemplary output of a model unit, in accordancewith one illustrative embodiment of the present disclosure;

FIG. 7 shows a flowchart of an implementation of a method, in accordancewith one illustrative embodiment of the present disclosure;

FIG. 8 shows an element of the flowchart of FIG. 7 in more detail, inaccordance with one illustrative embodiment of the present disclosure;

FIG. 9 shows another element of the flowchart of FIG. 7 in more detail,in accordance with one illustrative embodiment of the presentdisclosure;

FIG. 10 provides a block diagram of a computer executing instructionscontained in a non-transitory computer readable program storage mediumthat perform an implementation of a method, in accordance with oneillustrative embodiment of the present disclosure;

FIG. 11 shows the distributions of values of Seizure Intensity (Si),Seizure Duration (Sd) and Seizure Spread (Sc) (left columns) and thedistributions of natural logarithmic (Ln) transformations of theleft-column values (right columns), as discussed in more detail inExample 1.

FIG. 12 is Table 1 which contains detailed information about all thestimulation parameter configurations used in this trial.

While the disclosure is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the description herein of specificembodiments is not intended to limit the disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the appended claims.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Illustrative embodiments of the disclosure are described herein. Not allfeatures of an actual implementation are described. In the developmentof any actual embodiment, numerous implementation-specific decisionsmust be made to achieve the design-specific goals, which will vary fromone implementation to another. While possibly complex andtime-consuming, such a development effort would nevertheless be aroutine undertaking for persons of ordinary skill in the art having thebenefit of this disclosure.

This document does not intend to distinguish between components thatdiffer in name but not function. The terms “including” and “includes”are used in an open-ended fashion, and should be interpreted to mean“including, but not limited to.” “Couple” or “couples” is intended tomean either a direct or an indirect electrical connection. “Directcontact,” “direct attachment,” or providing a “direct coupling”indicates that a surface of a first element contacts the surface of asecond element with no substantial attenuating medium there between, butare not intended to exclude the presence of small quantities ofsubstances, such as bodily fluids, that do not substantially attenuateelectrical connections. The word “or” is used in the inclusive sense(i.e., “and/or”) unless a specific use to the contrary is explicitlystated.

“Electrode” or “electrodes” may refer to one or more stimulationelectrodes (i.e., electrodes for delivering a therapeutic signalgenerated by a medical device to a tissue), sensing electrodes (i.e.,electrodes for sensing a physiological indication of a state of apatient's body), and/or electrodes that are capable of both signalsensing and therapy delivery.

Turning to FIG. 1, a block diagram depiction of a medical device 100 isprovided, in accordance with one illustrative embodiment of the presentdisclosure. In some embodiments, the medical device 100 may beimplantable, while in other embodiments the medical device 100 may becompletely external to the body of the patient, while in still otherembodiments, some of its units may be implanted and others may beexternal.

Medical device 100 may comprise a controller 110 capable of controllingvarious aspects of the operation of the medical device 100. Thecontroller 110 may be capable of receiving internal data or externaldata, and in one embodiment, may be capable of causing a therapy unit120 to deliver a therapy for epilepsy to a patient's body or a partthereof. Generally, the controller 110 may be capable of affectingsubstantially all functions of the medical device 100.

The controller 110 may comprise various components, such as a processor115, a memory 117, etc. The processor 115 may comprise one or moremicrocontrollers, microprocessors, etc., capable of performing variousexecutions of software components. The memory 117 may comprise variousmemory portions where a number of types of data (e.g., internal data,external data instructions, software codes, status data, diagnosticdata, etc.) may be stored. The memory 117 may comprise one or more ofrandom access memory (RAM), dynamic random access memory (DRAM),electrically erasable programmable read-only memory (EEPROM), flashmemory, etc.

The medical device 100 may also comprise a power supply 130. The powersupply 130 may comprise a battery, voltage regulators, capacitors, etc.,to provide power for the operation of the medical device 100. Powersupply 130 may comprise a power source that in some embodiments may berechargeable. In other embodiments, a non-rechargeable power source maybe used. Power supply 130 may comprise a lithium/thionyl chloride cellor a lithium/carbon monofluoride (LiCFx) cell if the medical device 100is implantable, or may comprise conventional watch or 9V batteries forexternal (i.e., non-implantable) embodiments. Other battery types mayalso be used.

The medical device system depicted in FIG. 1 may also comprise one ormore sensor(s) 112. In the depicted embodiment, the sensor(s) 112 arecoupled via sensor lead(s) 111 to the medical device 100. In otherembodiments, the sensor(s) 112 can be in wireless communication with themedical device 100. Sensor(s) 112 are capable of receiving signalsrelated to a body parameter, such as the patient's brain activity (e.g.,electrical, chemical, cognitive), heart activity, blood pressure, and/ortemperature, among others, and delivering the signals to the medicaldevice 100. The sensor(s) 112 may also be capable of detecting a kineticsignal associated with a patient's movement. The sensor(s) 112, in oneembodiment, may be an accelerometer. The sensor(s) 112, in anotherembodiment, may be an inclinometer. In another embodiment, the sensor(s)112 may be an actigraph or a gyroscope. In one embodiment, the sensor(s)112 may be implanted in the patient's body. In other embodiments, thesensor(s) 112 are external structures that may be placed on thepatient's skin, such as over the patient's heart or elsewhere on thepatient's torso or limbs or on the scalp or brain. The sensor(s) 112, inone embodiment, may be a multimodal signal sensor capable of detectingvarious body signals, including cardiac signals associated with thepatient's cardiac activity and kinetic signals associated with thepatient's movement.

Alternatively or in addition, in one embodiment, the sensor(s) 112 maybe configured to detect signals associated with electrical activity ofthe patient's brain. For example, the sensor(s) 112 may beelectroencephalography (EEG) sensors or electocorticography (ECoG)sensors.

In other embodiments, a separate responsiveness and/or awareness unitmay be provided as part of the medical device or as a separate unit.

In addition or alternatively to body signals relating to dependentvariables, the sensor(s) 112 may be configured to detect signalsrelating to independent variables. Such sensor(s) 112 may sense bodysignals (e.g., a wake/sleep sensor) or non-body signals (e.g., a clockto sense time of day or other temporal factor(s)).

It should be borne in mind the sensor(s) 112 are optional, and need notbe present in every embodiment in accordance with the presentdisclosure.

The data determination unit 155 may be configured to determine at leastone dependent variable relating to each of a plurality of seizures in apatient. In one embodiment, the at least one dependent variable may bebased at least in part on at least one body signal, such as a bodysignal detected by sensor(s) 112. Alternatively or in addition, the datadetermination unit 155 may determine dependent variables based onsignals acquired by an external source (not shown) and communicated bythe external source to the data acquisition unit 155. Where a therapy isprovided to treat a seizure, the dependent variables may provide anindication of the effect of the therapy in some embodiments. Forexample, the dependent variables may identify whether the direction ofthe effect of the therapy is positive, negative, or substantiallyneutral, and/or the magnitude of the effect. “Substantially neutral”here means being neutral within dead band error and/or the detectionlimits of the system.

Any dependent variable(s) relating to seizures may be determined by thedata acquisition unit 155. In one embodiment, the at least one dependentvariable may be selected from intensity of the seizure, duration of theseizure, extent of spread of the seizure, seizure severity index, anadverse effect (e.g., intolerability or lack of safety) of a therapy,and time elapsed between a seizure and the next seizure(s) or between aseizure and previous seizures, referred to herein as inter-seizureintervals. Inter-seizure interval may be determined by computing thetime elapsed between the onset of two or more seizures or between theend of a seizure and the onset of the next. Other dependent variablesinclude, but are not limited to, efficacy of a therapy, side effects ofa therapy, and tolerability of a therapy.

In an embodiment wherein the at least one dependent variable may bebased at least in part on at least one body signal, an intensity of aseizure may be determined from a peak heart rate above a referenceinterictal heart rate or an area under the curve of heart rate elevationabove a reference interictal heart rate, among other body signals and/orvalues calculable from body signals. A duration of a seizure may bedetermined from a duration of a heart rate elevation above a referenceinterictal heart rate, among other sources. A heart rate elevation abovea reference interictal heart rate, among other sources, may also be usedto determine a time elapsed between seizures (e.g, most recent seizureand the next seizure). An extent of spread of a seizure may bedetermined from kinetic signals indicative of movement of various limbsor signals relating to the patient's responsiveness or awareness, fromthe number of organs or functions affected by a seizure (e.g., brain,heart, breathing, metabolic, etc.).

More information regarding detection of epileptic events anddetermination of severity and location in the body of epileptic eventscan be found in U.S. patent application Ser. No. 12/756,065, filed Apr.7, 2010; U.S. patent application Ser. No. 12/770,562, filed Apr. 29,2010; U.S. patent application Ser. No. 12/896,525, filed Oct. 1, 2010;U.S. patent application Ser. No. 13/040,996, filed Mar. 4, 2011; U.S.patent application Ser. No. 13/091,033, filed Apr. 20, 2011; and U.S.patent application Ser. No. 13/098,262, filed Apr. 29, 2011; all ofwhich are hereby incorporated herein by reference in their entirety.

Any or all of these dependent variables may also be determined from EEGor ECoG of the patient's brain or from cognitive activity.

The intensity of the seizure, the duration of the seizure, and theextent of spread of the seizure, may be taken together to yield a metricof seizure severity, e.g., a seizure severity index. Seizure severitythus may be based on at least one of the dependent variables referred toabove. In one embodiment, seizure severity may be defined as one-thirdof the sum of the standardized natural log of intensity of the seizure,the standardized natural log of duration of the seizure, and thestandardized natural log of extent of spread of the seizure. In anotherembodiment, seizure severity may be expressed as the sum of at least oneof intensity or duration of change from baseline activity in each organor function impacted by the seizure, multiplied by the total number oforgans or functions impacted by the seizure.

The data quantification unit 165 may be capable of quantifying at leastone relationship between at least one of the plurality of dependentvariables and at least one of the plurality of independent variables. Inone embodiment, the at least one independent variable may be selectedfrom whether the seizure was treated with at least one therapy, whetherthe previous seizure was treated with at least one therapy, the type oftherapy, the drug/chemical dose, current density in an electricaltherapy, or degree of temperature change induced by a thermal therapy,the target of the therapy, a time elapsed since a previous treatmentwith at least one therapy, a number of seizures since a previoustreatment with at least one therapy, a time of day the seizure occurred,a time of the month (or year) the seizure occurred, the level ofconsciousness of the patient (e.g., awake vs. asleep); the level andtype of cognitive activity, the patient's health state, or two or morethereof.

In one embodiment, each independent variable may comprise a therapyparameter, a therapy delivery parameter, a temporal factor, anenvironmental factor, or a patient factor.

The data quantification unit 165 may be capable of quantifying using anymathematical technique. In one embodiment, quantifying may comprise anyform of regression analysis or models on said at least one dependentvariable and said at least one independent variable. In anotherembodiment, any form of analyses or modeling applicable to multivariatesituations or phenomena of efficacy of therapy as a function of type oftherapy, (e.g., drug, electrical stimulation), one or more parametersdefining the therapy (e.g., dose or quantity, duration or frequency ofsaid therapy, current, pulse width, on time, off time), the number ofdifferent therapies being delivered, the delivery modality (e.g.,continuous, contingent, periodic or randomly-timed), the time of day,month, season, or year of delivery, the anatomical target of thetherapy, the conditions of a patient during delivery (e.g., awake orasleep, cognitively active or inactive, physically active or inactive,healthy or ill, fasting or non-fasting, ingestion of alcohol or ofpsychoactive drugs, duration and type of light exposure, stress level,psychiatric/emotional state, menses, ovulation or pregnancy in the caseof women among other independent variables).

In yet another embodiment, regression analyses of any form or any othertype of suitable analyses may applied to assessing adverse effects of atherapy as a function of type of therapy, the dose or quantity, durationor frequency of said therapy, the number of different therapies beingdelivered, the delivery modality (e.g., continuous, contingent, periodicor randomly-timed), the time of day, month or year of delivery, theanatomical target of the therapy, the conditions of a patient duringdelivery (e.g., awake or asleep, cognitively active or inactive,physically active or inactive, healthy or ill, fasting or non-fasting,ingestion of alcohol or of psychoactive drugs, duration and type oflight exposure, stress level, psychiatric/emotional state, menses,ovulation or pregnancy in the case of women among others).

The data quantification unit 165 may be capable of quantifying any of anumber of effects of an independent variable on a dependent variable. Inone embodiment, the at least one effect may be the presence of serialcorrelation between dependent variables. In other words, the at leastone effect may be the extent to which seizure intensity, seizureduration, extent of seizure spread, and/or time elapsed between seizuresdepends on previous seizures. In another embodiment, in which treatmentwith at least one therapy may be performed, the effect may be animmediate result of the treatment. “Immediate” in this context refers tothe period of time encompassed by the delivery of a therapy (forelectrical and thermal modalities) or the half-life of a drug in tissueor serum (for a drug or chemical modality). In another embodiment, inwhich treatment with at least one therapy may be performed, the effectmay outlast the time encompassed by the delivery of a therapy, alsoreferred to as a “carry-over” effect.

In other embodiments, in which treatment with at least one therapy maybe performed, one or more longer-time scales, including but not limitedto an entire period for which data is available, may be considered.

The relationships quantified in embodiments of this disclosure mayrelate to individual seizures, groups of seizures, or all seizures forwhich data is available.

In another embodiment (not shown), the medical device 100 may comprise adata quantification/qualification unit, which may be capable ofquantifying one or more relationships, as discussed above, and mayalternatively or in addition be capable of qualifying one or morerelationships.

The data classification/ranking unit 167 may be capable of performing afurther classification or ranking of a plurality of independentvariables, a plurality of dependent variables, or both on aquantification of a relationship determined by the data quantificationunit 165. The data classification/ranking unit 167 is optional and neednot be present in every embodiment according to the present disclosure.The therapy unit 120 may be capable of administering at least onetherapy to a patient. Any therapy may be administered. In oneembodiment, the at least one therapy may be selected from an electricalstimulation of a target structure of the brain, an electricalstimulation of a target portion of a cranial nerve, a drug, a thermaltreatment of a target portion of a neural structure, a noxious ornon-noxious sensory therapy to a sensory organ (e.g., eyes, ears, nose,mouth, skin pressure sensors, skin pain sensors, etc.), a cognitivetherapy to the patient, or two or more thereof. In a particularembodiment, the at least one therapy may be an electrical stimulation tothe brain, a cranial nerve, or both of the patient. Cognitive (e.g.,“biofeedback”) therapies may be also administered.

The target structure of the brain may, but need not, be the entirebrain. The target portion of the cranial nerve may by the entire cranialnerve, but need not be. The target portion of the neural structure mayby the entire neural structure, but need not be.

More information regarding exemplary embodiments of electricalstimulation of a cranial nerve may be found in U.S. Pat. Nos. 4,702,254,4,867,164, and 5,025,807 to Dr. Jacob Zabara, and in U.S. Pat. Nos.6,961,618 and 7,457,665 to Ivan Osorio and Mark G. Frei, all of whichare hereby incorporated by reference in their entirety.

It should be borne in mind the therapy unit 120 is optional, and neednot be present in every embodiment in accordance with the presentdisclosure.

The efficacy assessment unit 175 may be capable of assessing a magnitudeof efficacy of at least one therapy. In one embodiment, the efficacyassessment unit 175 may take as an input a quantified effect from thedata quantification unit 165 and determine an efficacy based at least inpart on the quantified effect. For example, if the data quantificationunit 165 quantifies a reduction in seizure severity for treated seizuresand no change in seizure severity for untreated seizures, the efficacyassessment unit 175 may return a result indicating the treatment may beconsidered efficacious. In one embodiment, the efficacy assessment unit175 may quantify the efficacy as part of the assessment.

The efficacy assessment unit 175 may be capable of determining if theeffect is positive (beneficial), negative (detrimental) or neutral (noeffect) on at least one of a seizure intensity, duration, extent ofspread, seizure severity index, or inter-seizure interval length(s).

The efficacy assessment unit 175 may be capable of ranking (e.g. fromhighest to lowest) and classifying as positive, negative or neutral, theeffect of a therapy on at least one of a seizure intensity, duration,extent of spread or inter-seizure interval length(s).

In one embodiment, the efficacy assessment unit 175 may be considered aresponsive action unit.

It should be borne in mind the efficacy assessment unit 175 is optional,and need not be present in every embodiment in accordance with thepresent disclosure.

The logging unit 185 may be capable of storing and/or reporting one ormore values determined by the data determination unit 155, the dataquantification unit 165, the efficacy assessment unit 175, the therapyunit 120, or two or more thereof. In one embodiment, the logging unit185 may be considered a responsive action unit. The logging unit 185 isoptional, and need not be present in every embodiment in accordance withthe present disclosure.

In one embodiment, one or more responsive actions may be performedmanually. Alternatively or in addition, one or more responsive actionsmay be performed automatically.

One or more of the blocks illustrated in the block diagram of themedical device 100 in FIG. 1 may comprise hardware units, softwareunits, firmware units, or any combination thereof. Additionally, one ormore blocks illustrated in FIG. 1 may be combined with other blocks,which may represent circuit hardware units, software algorithms, etc.Additionally, any number of the circuitry or software units associatedwith the various blocks illustrated in FIG. 1 may be combined into aprogrammable device, such as a field programmable gate array, an ASICdevice, etc.

In on embodiment of the present disclosure, principal component analysisor a similar/related analysis may be performed to determine the numberof components (e.g., dependent variables), subject to variability of thedata (e.g., seizure intensity, duration, or extent of spread) as afunction of at least one of the therapy independent variables.

FIG. 2 shows a flowchart of an implementation of a method. Selection ofa plurality of dependent variables relating to a seizure in a patientmay be performed at 210. The plurality of dependent variables may beselected from intensity of the seizure, duration of the seizure, extentof spread of the seizure, seizure severity index, a seizure frequency,an adverse effect of a therapy, and the inter-seizure interval(s),referred to in the Example below as “Time Between Seizures” (TBS). Inone embodiment, the plurality of dependent variables is selected frominter-seizure interval or seizure severity. In one embodiment, a seizureseverity index may be defined as one-third of the sum of thestandardized natural log of intensity of the seizure, the standardizednatural log of duration of the seizure, and the standardized natural logof extent of spread of the seizure. Other seizure severity measures maybe used.

A plurality of independent variables may also be selected at 210.

If a determination is made at 220 that the dependent variables may nothave been obtained for a desired number of seizures, (e.g., the numberof seizures for which values of the dependent variables has beenacquired may be too low to be clinically or statistically significant),flow returns to select a plurality of variables at 210.

After 220, if the dependent variable has been selected for a desirednumber of seizures, quantification of at least one relationship betweenat least one of the plurality of dependent variables and at least one ofthe plurality of independent variables may be performed at 230. Theplurality of independent variables may be selected from whether theseizure was treated with at least one therapy, whether a priorseizure(s) was(were) treated with at least one therapy, a time elapsedsince a prior treatment with at least one therapy, a number of seizuressince a prior treatment with at least one therapy, a type of therapy, adose of a therapy (in the case of drugs), a current density (in the caseof electrical stimulation), a degree of tissue cooling or warming (inthe case of thermal energy), a time of day the seizure occurred, a timeof month the seizure occurred, a time of year the seizure occurred, alevel of consciousness (e.g., awake or asleep), a level and type ofcognitive activity, a level an type of physical activity, a state ofhealth, the concentration of medicaments or chemicals in a tissue, ortwo or more thereof. The at least one relationship may be selected froman immediate result of a treatment with at least one therapy, a“carry-over” effect of a treatment with at least one therapy, or two ormore thereof. The quantification at 230 may comprise regression analysison the dependent variables and the independent variables.

If at least one therapy is performed, such as an electrical stimulationof the brain, an electrical stimulation of a cranial nerve, a drug, athermal treatment of a neural structure, or two or more thereof, anefficacy of the therapy may be assessed at 240.

It should be borne in mind that therapy need not be performed and isoptional. Thus, an assessment at 240 need not be performed in everyembodiment in accordance with the present disclosure.

After an assessment of efficacy of the therapy, if any, at 240, flowthen returns to the acquisition at 210.

Turning to FIG. 3, in a patient's brain 310, one or more independentvariable(s) 320 may be considered as inputs to a patient seizurefunction 330. In one embodiment of this disclosure, the patient seizurefunction 330 may be considered a “black box.” Therefore, in oneembodiment, characterization or analysis of the output of the black boxmay be useful in determining the activity of the black box and usingthis in turn to affect therapy. The patient seizure function 330 may beconsidered to generate outputs relating to the patient's seizures asdependent variables 340. Specific classes of independent variables 320include, but are not limited to, therapy parameters 322, temporalfactors 324, environmental factors 326, and patient factors 328, amongothers. FIG. 4 lists exemplary independent variables 320 in more detail.In embodiments wherein a plurality of independent variables 320 may beselected, each independent variable 320 may be independently selectedfrom any branch and any level of the branching depicted in FIG. 4. Insome embodiments, therapy parameters defining one or more types oftherapy may be included as independent variables. In some embodiments,environmental factors, patient factors, and/or temporal parameters maybe included as independent variables to assess their effect upon seizuredependent variables.

Specific classes of dependent variables 340 include, but are not limitedto, seizure effect data 342 or patient effect data 344 (e.g., adverseeffects, safety, tolerability).

Independent variables 320 and dependent variables 340 may be inputs to amodel unit 350. The model unit 350 may be capable of performing one ormore analyses to quantify and classify (e.g., positive effect) arelationship between at least one independent variable(s) 320 and atleast one dependent variable(s) 340. The model unit 350 may be capableof performing a regression analysis. In statistics, regression analysisincludes any techniques for modeling and analyzing several variables,when the focus is on the relationship between a dependent variable(e.g., therapeutic efficacy or adverse effects) and one of a pluralityof independent variables (e.g., the effect of varying the frequency ofelectrical stimulation on at least one of seizure frequency, intensity,etc.). Regression analysis provides information of how a dependentvariable changes as a function of changes in one of the independentvariables, while keeping other independent variables constant. Lesscommonly, the focus is on a quantile, or other location parameter of theconditional distribution of the dependent variable given the independentvariables. In regression analysis, it is also of interest tocharacterize the variation of the dependent variable around theregression function, which can be described by a probabilitydistribution.

Alternatively or in addition, the model unit 350 may be capable ofperforming other methods of analysis. Other methods that may be usedinclude, but are not limited to, Bayesian methods (e.g. Bayesian linearregression), least absolute deviations/quantile regression,nonparametric regression, distance metric learning, Pearsonproduct-moment correlation coefficient, or fraction varianceunexplained, among others.

Whatever analysis method may be used, the model unit 350 generates amodel output 360. The model output 360 may comprise a vector (e.g., amagnitude and direction), a vector field, a scalar, or two or morethereof.

Exemplary model outputs 360 are shown in FIG. 5 and FIG. 6.

FIG. 5 depicts a table reflecting hypothetical, exemplary relationships(shown as vectors having magnitudes and directions) between each ofthree independent variables (electrical stimulation therapy currentamplitude, electrical stimulation therapy pulse width, and electricalstimulation therapy pulse frequency) on a dependent variable (seizureintensity). The relationship may hold for seizures occurring during anentire time period for which data is available, or it may relate to onlyseizures occurring during a time window within the entire time period.The relationships in FIG. 5 are illustrative only, and are not intendedto reflect relationship embodied in actual patient data.

The three vectors may be used to rank the three independent variables bythe magnitude and direction of the vectors. In the hypothetical exampleshown in FIG. 5, vector #2, representing a relationship between pulsewidth and seizure intensity, is greater than vector #3, representing arelationship between pulse frequency and seizure intensity, which inturn is greater than vector #1, representing a relationship betweencurrent amplitude and seizure intensity. From these comparisons betweenthe vectors, the three independent variables (in this example, pulsewidth, pulse frequency, and current amplitude) may be ranked based ontheir magnitude and “direction” (e.g., positive/beneficial,negative/detrimental). The vectors and the rankings may be consideredindicators of which independent variables have the strongestrelationship with the dependent variable, i.e., which independentvariables have the greatest impact on the dependent variable. From this,a physician may conclude the highest ranked independent variables arethe ones that may most fruitfully be adjusted to enhance desirableeffects on the dependent variable (e.g., in the hypothetical example ofFIG. 5, a doubling in pulse width may yield a greater reduction inseizure intensity than would be expected for a doubling in pulsefrequency). The therapeutic value of an independent variable is notsolely determined by its ranking but also by its effect (e.g., positiveor negative) on a dependent variable. For example, in the face of onlytwo possible choices (a highly ranked independent variable with anegative effect vs. a low ranking variable with a beneficial effect),selecting the weakly positive one may be desirable. Alternatively or inaddition, if an independent variable over which the physician has nocontrol (e.g., time of day) is ranked highly, the physician may concludethat therapy may have less or more impact during this time period thanotherwise and take into account when assessing efficacy.

Quantifying one or more relationships, and/or the magnitude anddirection of a relationship between independent and dependent variable,and/or ranking them, may allow a narrowing of the search space duringoptimization of a therapy, thus expediting the “identification” of abeneficial therapy. Another benefit may be finding optimal ranges of aparticular therapy parameter under certain circumstances (e.g. at aparticular time of day), which may allow extending battery life (bykeeping an electrical stimulation therapy parameter below a point ofdiminishing or detrimental returns) and/or improving efficacy (bykeeping an electrical stimulation therapy parameter above a point wherethe stimulation may have too low an energy to be efficacious).

FIG. 6 depicts a table reflecting other hypothetical, exemplaryrelationships (shown as vectors having magnitudes and directions(positive or negative) between one independent variable (e.g., currentamplitude in this example) and a plurality of dependent variables(seizure intensity, seizure duration, extent of seizure spread,inter-seizure interval, and seizure severity) over two different timescales discussed elsewhere herein, an immediate time frame and acarryover time frame. The magnitudes shown here are normalized and arerelative to a baseline case without electrical stimulation. In otherembodiments, normalization may not be performed. Generally, forelectrical stimulation therapy, the independent variables would not beexpected to have any immediate effect upon ISI, except for the raresituation where stimulation duration exceeds the duration of theseizure. FIG. 6 also depicts a situation wherein an independent variablemay have an immediate detrimental effect on one or more seizurevariables), but has a beneficial “carry over” effect on one or morefuture seizures.

The relationships in FIG. 6 are illustrative only, and are not intendedto reflect relationship embodied in actual patient data.

Turning now to FIG. 7, a flowchart is presented depicting a performanceof a method according to one illustrative embodiment of the presentclaims. At least one independent variable relating to the patient'sseizures may be received at 710. “Received” encompasses reception ofstored data in a memory, a local database, a remote database, etc.; adetermination from sensed data, or a derivation/transformation fromsensed data.

Similarly, at least one dependent variable relating to the patient'sseizures may be received at 720. The reception of the dependentvariable(s) may involve the same or a different technique as thereception of the independent variable(s).

However received, the independent variable(s) and dependent variable(s)may be provided to a model at 730. The model may be capable ofperforming one or more of the analyses discussed in reference to themodel unit 350 depicted in FIG. 3, above.

The model may then be used to determine at least one relationshipbetween the independent variable(s) and the dependent variable(s) at740. The relationship(s) may be represented by a vector, a vector field,a scalar, or two or more thereof.

FIG. 8 shows the determining at 740 in more detail. A mathematicalfunction may be selected at 810 and applies at 815 to determine therelationship between independent variable(s) and dependent variable(s).The results at 820 may comprise a vector (e.g., a magnitude and adirection), a vector field, a scalar, or two or more thereof.

Though not shown, the results at 820 may be considered in characterizingthe relationship in qualitative terms, i.e., a vector having a valuegreater than about +0.4 may be qualified as a “significant positive”relationship.

Returning to FIG. 7, after 740, flow may follow either or both of twodifferent routes. In one route, a decision is made at 745 whether themodel is adequate. This decision may be made based on whether thegoodness of fit of the model has a value that meets or exceeds apredetermined standard of adequacy. If the model is found inadequate, insome embodiments, the model may be adjusted at 760. Adjustment of themodel may comprise adding or removing one or more independentvariable(s), adding or removing one or more dependent variable(s),selecting one or more alternative and/or additional analysis methods,changing the mathematical function describing the relationship betweenthe independent variable(s) and the dependent variable(s), or two ormore thereof, among others. After adjustment at 760, flow may return tothe determination of the at least one relationship at 740.

If the model is found adequate at 745, flow passes to a decision to bemade at 750 as to whether the relationship is statistically orclinically significant. If it is, the existing model and/or treatmentparameters may be retained at 770, from where flow may loop back to 710.If the relationship is found to not be statistically or clinicallysignificant, treatment parameters may optionally be adjusted at 790(discussed in more detail below).

In one embodiment, a “bottom up”/incremental approach to regressionanalysis may be followed, wherein the first analysis may be between oneor a few independent variable(s) and one or a few dependent variable(s).The strength of their relationship may be assessed (using for example ascatter plot) and, if weak/unsatisfactory, the number of independentvariables may be increased as needed to improve the fit. In someinstances, the fit may not improve despite increasing the number ofindependent variables as a cause of the poor fit. In such instances,provided the “omitted variable bias” has been excluded, the possibilitythat the relationship between independent and dependent variables may bea) non-linear or b) unstable (the problem of “random” or “time varying”coefficients) may then be addressed.

In an alternative embodiment (not shown in FIG. 7), a “topdown”/decremental approach to regression analysis may be followed,wherein all/most independent variables may be initially included in theanalysis. If the model is adequate to identify satisfactoryrelationships between the independent and dependent variables, thenumber of variables may be reduced. If this action degrades the fit, thereduction may be stopped and the model may be endowed with the smallestnumber of independent variables that preserve “goodness” of the fit. Anadvantage of this “top down” approach may be a higher probability ofrapidly making valid/meaningful interpretation of results than the“bottom up” or incremental approach, but at a higher computational cost.

Returning to FIG. 7, a ranking may be performed at 780. The ranking maycomprise a ranking of independent variables and/or a ranking ofdependent variables based on the magnitude, direction, and/or andclassification as positive/beneficial, negative/detrimental, orsubstantially neutral. The latter ranking and classification may yieldinformation suitable for determining which dependent variables are mostsusceptible to change upon changes in independent variables. Thisinformation may guide the ranking of independent variables to beperformed for one or a few dependent variables, but need not.

In other embodiments, all effects in a set or subset of seizure data maybe ranked based on a single dependent variable. The rankings may benormalized to a standard scale, e.g., 0-1 in some embodiments, while inother embodiments more useful information may be obtained withoutnormalizing the rankings. Normalized rankings may be helpful in analysisof seizures for which a non-normalized output would lack clinicalsignificance. For example, a convulsion typically has higher dependentvariable rankings than a complex partial seizure. If attention is onlypaid to a hypothetical, exemplary, and illustrative only observationthat both the convulsion and complex partial seizure saw decreases(benefits) in seizure duration by 0.5, the following difference inclinical impact would be overlooked. Even if its duration decreased from10 minutes to 5 minutes, a convulsion of the latter duration wouldremain a convulsion. On the other hand, a reduction in the duration of acomplex partial seizure from 1 min to 0.5 min may be sufficient for whatwould have been a complex partial seizure to instead only manifest as asimple partial seizure. The simple partial seizure would be expected topreserve the patient's awareness (a beneficial effect), which a complexpartial would not.

FIG. 9 shows one embodiment of the ranking at 780 in more detail. In thedepicted embodiment, at least one dependent variable(s) may be selectedat 910 for which ranking of independent variables is desired. The atleast one dependent variable(s) may be selected based on the magnitudeand direction, and/or it may be selected based on other reasons.

A “scale” for ranking (e.g., a vector, a vector field, or a scalar, andwhich specific one if multiple vectors, etc. are available) may beselected or otherwise provided at 920. The independent variable(s) maybe ranked/classified according to the ranking scale at 930. The rankingmay be stored and/or reported at 940.

Returning to FIG. 7, after ranking is performed at 780 and/or after arelationship is found to be not significant at 750, flow may then passto an optional adjustment of treatment parameters at 790. This optionaladjustment of one or more characteristics of treatment parameters may bebased at least in part on the ranking at 780.

In one embodiment, a method according to the present disclosurecomprises selecting a plurality of dependent variables relating to eachof a plurality of seizures in a patient; selecting a plurality ofindependent variables, wherein each independent variable comprises atherapy parameter, a therapy delivery parameter, a temporal factor, anenvironmental factor, or a patient factor; quantifying at least onerelationship between each of at least two dependent variables and eachof at least two independent variables; and performing an action inresponse to said quantifying, selected from reporting said at least onerelationship, assessing an efficacy of a therapy, assessing an adverseeffect of said therapy, providing a therapy modification recommendation,or adjusting said therapy.

As an illustrative example, quantifications of relationships may be madebetween a first independent variable (IV-1) and a first dependentvariable (DV-1), as well as between IV-1 and a second dependent variable(DV-2). Similarly, quantifications of relationships may be made betweena second independent variable (IV-2) and DV-1, as well as between IV-2and DV-2. Similarly, the quantifications of relationships may be madebetween DV-1 and IV-1, as well as DV-1 and IV-2. Similarly,quantifications of relationships may be made between DV-2 and IV-1, aswell as between DV-2 and IV-1. In one embodiment, the method shown inFIG. 2 or 7 may be performed by a computer executing instructionscontained in a non-transitory computer readable program storage medium.The computer may be a desktop, laptop, PDA, cellphone, tablet computer,or the like. For example, FIG. 10 depicts a computer 1000 performing themethod shown in FIG. 7.

Methods may be implemented in a variety of ways including, but notlimited to, machine readable storage devices, various processor-basedand computer devices, mechanical devices and/or in networks/systems, andthe like. Devices may be internal to a patient or external, andassessing efficacy may likewise be conducted internally in a device, orexternally in a separate device that is either communicatively coupledto an internal device or a standalone device. Ways of assessing atherapeutic efficacy of seizure treatment may be done using any or allof those described herein in addition to similar methods that wouldbecome apparent to those of skill in the art having the benefit of thisdisclosure. The terms “assessing,” “treating,” “determining,” and/or anyother terms used in claims, may be done by direct and/or indirectapproaches.

In one embodiment, the present disclosure discloses a method that maycomprise treating a seizure using at least one of contingent, periodicor randomly-timed delivery of electrical stimulation; determining atleast one parameter (e.g., an independent variable) associated with theseizure; and assessing a therapeutic efficacy of seizure treatment usingthe at least one parameter.

In one embodiment, the present disclosure discloses an apparatus thatmay comprise a module adapted to treat a seizure using at least one ofcontingent, periodic or randomly-time electrical stimulation; a moduleadapted to determine at least one parameter associated with the seizure;and a module adapted to assess a therapeutic efficacy of seizuretreatment using the at least one parameter.

In one embodiment, the present disclosure discloses a non-transitive,computer program storage device containing instructions for performingthe method that may comprise treating a seizure using at least one ofcontingent, periodic or randomly-timed electrical stimulation;determining at least one parameter associated with the seizure; andassessing a therapeutic efficacy of seizure treatment using the at leastone parameter.

In one embodiment, the present disclosure discloses a processor-basedcomputing device that may comprise a processor; and a module coupled tothe processor, the module adapted to: treat a seizure using at least oneof contingent, periodic or randomly-timed electrical stimulation;determine at least one parameter associated with the seizure; and assessa therapeutic efficacy of seizure treatment using the at least oneparameter.

In one embodiment, the present disclosure discloses a system, comprisinga first module adapted to treat a seizure using at least one ofcontingent, periodic or randomly-timed electrical stimulation; a secondmodule coupled to the first module, the second module adapted todetermine at least one parameter associated with the seizure; and athird module coupled to at least one of the first module or the secondmodule, the third module adapted to assess a therapeutic efficacy ofseizure treatment using the at least one parameter.

In one embodiment, the present disclosure discloses an implantablemedical device comprising at least one of a seizure treatment unitcapable of using at least one of contingent, periodic or randomly-timedelectrical stimulation, a determination unit capable of determining atleast one parameter associated with the seizure, or an assessment unitcapable of assessing a therapeutic efficacy of seizure treatment usingthe at least one parameter.

The following examples are included to demonstrate preferred embodimentsof the disclosure. It should be appreciated by those of skill in the artthat the techniques disclosed in the examples which follow representtechniques discovered by the inventor to function well in the practiceof the disclosure, and thus can be considered to constitute preferredmodes for its practice. However, those of skill in the art should, inlight of the present disclosure, appreciate that many changes can bemade in the specific embodiments which are disclosed and still obtain alike or similar result without departing from the spirit and scope ofthe disclosure.

Example 1: Towards a Quantitative Multivariate Analysis of the Efficacyof Anti-Seizure Therapies

(The text, tables, and figures of Example 1 have been published by I.Osorio, et al., Epilepsy & Behavior 18 (2010) 335-343, available onlineon Jun. 11, 2010).

Abstract

Seizure frequency is the only variable used for assessing therapeuticefficacy. Advances in quantitative analyses allow measurement ofintensity, duration, spread and time between seizures (TBS). Thesevariables are used here to investigate the efficacy of brain electricalstimulation in humans with pharmaco-resistant seizures.

The results of a trial of contingent high frequency electricalstimulation (HFES) for abatement of clinical and subclinical seizuresare examined using Principal Component analysis (PCA) and regressionmodels.

HFES significantly: a) Decreased seizure severity in 2/8 and increasedTBS in 1/8 subjects; b) Decreased seizure severity in the primaryepileptogenic zone of one subject but increased it in the secondaryzones; c) Had a beneficial effect on severity (reduction) and TBS(increase) in the remainder. These effects were immediate and alsooutlasted the duration of stimulation (“carry-over”).

Contingent HFES has multifarious and complex effects, intra- andinter-individually, on seizure severity and TBS. Two inferences, at oncepromising and sobering may be drawn from these results: one, thatcontingent electrical stimulation deserves a place in the armamentariumof therapies for pharmaco-resistant seizures, and the other that itsapparently narrow therapeutic ratio calls for careful implementation andmultivariate quantification of its effects.

Introduction

The assessment of efficacy of anti-seizure treatments and indirectly thefoundations of clinical epileptology rest on seizure diaries, which arenot only grossly inaccurate [1,2] but also fail to quantify relevantvariables such as intensity, duration and extent of spread that providea measure of seizure severity and would allow assessment of theirevolution over long time scales.

Technological advances [3-6] currently allow quantification ofintensity, duration, extent of spread (number of electrode contactsregistering seizure activity), and of the time between seizures (TBS).The observation that successive seizure occurrence and severity may becorrelated [7,8] and that seizure intensity, duration, spread and TBSappear to respond differentially to electrical currents (as will beshown below), underscore the importance of quantifying these variablesand of multivariate analysis of the effects of anti-seizure therapies.

Brain electrical stimulation (BES) for treatment of pharmaco-resistantepilepsy is the subject of intense research interest. An in-patienttrial of contingent high frequency (>100 Hz) BES in subjects withpharmaco-resistant epilepsy showed marked reduction in clinical seizures[6]. Although the afore-referenced trial is quite small, its depth ofanalysis and attention to relevant details (all automated detections andstimulations were visually reviewed by an independent expert) cannot bematched by larger trials since they rely mainly [9] or solely [10] onseizure diaries for assessment of efficacy.

Epileptic seizures are subject to multiple sources of variation (i.e.,circadian, sleep-wake cycle, hormonal, drug serum concentrations, etc.),and as a consequence appropriate statistical tools are required fortheir objective characterization.

Through the application of principal component analysis [11] and linearregression models [4, 12] to the in-patient trial data [6], this studyderives quantitative seizure descriptors that take into account not onlythe effects of therapy but also potentially confounding factors such astheir intrinsic variability in frequency, circadian cycle influences [4,13] and the presence of serial correlation [7, 8] among seizures. Themeasures thus derived have useful clinical applications and providevaluable insight into the complex and multifarious effects of highfrequency electrical stimulation on seizures.

Methods and Materials

This in-patient trial was conducted at the University of Kansas MedicalCenter, with approval from the Human Subjects Committee and the FDA (IDEG9990238) on eight subjects with pharmaco-resistant epilepsiesundergoing invasive evaluation for possible surgery [6]. Subjects wereenrolled in the order of admission.

The trial consisted of a control phase which corresponded to an invasivesurgical evaluation, followed by an experimental phase during whichautomated seizure blockage using electrical stimulation was attempted.The durations of the phases varied for each individual. Anti-seizuredrugs were either discontinued or their dosage decreased at admission;their reintroduction, increases in dosage, or the use of rescuemedications at any time during the experimental phase would have led totermination of the subject's participation in the trial and exclusion ofthe data from analysis.

Electrodes were placed intracranially in all subjects before the controlphase. Each subject served as her/his own control and was assigned toone of two groups: 1. The Local Closed-Loop, or 2. Remote Closed-Loop.In 4 subjects assigned to the “local” group, seizures originated from asingle discrete area and were thus good surgical candidates. In thesesubjects, high frequency electrical stimulation was delivered at or nearthe epileptogenic zone, and the electrocorticogram (ECoG) was acquiredthrough contacts adjacent to those used for stimulation. In 4 subjectsincluded in the “remote” group, seizures originated independently fromtwo or more sites. In these inoperable subjects, electrodes (MedtronicDBS 3387-40; Medtronic, Minneapolis, Minn.) were implantedstereotaxically into each anterior thalamic nucleus after completion ofthe control phase for delivery of high frequency electrical pulses.These subjects were allowed to recover from general anesthesia for 24hours before initiation of the experimental phase. ECoG was recordedthrough the electrodes used for localization of the epileptogenic zones.

ECoG was analyzed with a previously validated algorithm [3, 5] thatquantifies seizure intensity, duration, and extent of spread; thisalgorithm's output was the basis for delivery of electrical stimulationto every other detected seizure. For this study, seizures were definedas any automated detection reaching an intensity threshold, T=22, for aminimum duration, D=0.84 seconds [3, 5] with or without clinicalmanifestations. All detections were verified a posteriori through visualanalysis performed by an independent expert reviewer. Seizures wereclassified as either clinical (visible manifestations or event buttonactivation) or electrographic (no visible manifestations or event buttonactivation). The procedures for capturing and verifying automateddetections (true positive vs. false positive) or event button presses,and for grouping multiple seizure detections into a single detectioncluster if separated by less than 60 s, were described in detail in aprevious publication [5].

Hardware, Software, Stimulation Parameters, and Procedures to AssessSafety and Tolerability

A system for real-time seizure detection and contingent delivery ofelectrical currents (Flint Hills Scientific, Lawrence, Kans.) [14] wasplaced at the bedside and connected through optical isolation tocommercial video-EEG equipment (BMSI, Nicolet) and to a constant currentstimulator (S-12; Grass Instruments, West Warwick, R.I.) for controlleddelivery of biphasic, charge-balanced square pulses. High frequencyelectrical stimulation (HFES) was defined as 100 Hz minimum (forrationale see [6]), and charge density (pulse width was 100 or 200usec/phase) for each set of parameters remained below the acceptedsafety limit of 30 uC/cm²/phase [15]. FIFES duration was 1 s. for thelocal closed-loop group and 30 s. for the remote closed-loop group, withthe option of redelivery limited, for safety, to a maximum of fivere-stimulations per detection.

For the “local” group, mean stimulation frequency was 251.5 Hz (range:50-500 Hz), mean intensity was 5 mA, and mean duration was 1 s. For the“remote” group, mean stimulation frequency was 151.5 Hz (range: 100-200Hz), mean intensity was 5.4 mA (range: 1.5-8.5 mA), and mean durationwas 21.75 seconds (range: 2.5-30 seconds). Table 1 (see FIG. 12) andreference [6] contains detailed information about all the stimulationparameter configurations used in this trial.

Parameter configurations for electrical stimulation. Each configurationrefers to a unique combination of frequency f (Hz), intensity I (mA),duration D (s), pulse width PW (us/phase) and location. Onlyconfigurations in which one or more true positive seizure detectionclusters were stimulated are reported. Location code refers to specificcontacts chosen for stimulation. The number of clusters may differslightly from that given in Table 2 because certain model parameters(e.g., PStim, SStim) cannot be determined for clusters at the start/endof a stim configuration or near a gap in data collection.

Behavior, vital signs, SaO₂, and EKG were monitored before, during, andafter at least three manually triggered stimulations, separated by atleast 2 minutes, at the initiation of the experimental phase andwhenever any of the stimulation parameters was increased. Ifstimulations elicited adverse effects such as after discharges,intensity was decreased by 0.5 mA until a safe/tolerable level wasreached. If intensity, duration, or spread were not reduced by at least50% after two stimulation trials, the frequency was increased by 50 Hzuntil any of the following occurred: (1) the desirable effect wasobtained; (2) the predetermined frequency limit for the “local” (500 Hz)or the “remote” group (200 Hz) was reached; or (3) there were adverseeffects. A parameter configuration set consisted of a pre-specifiedstimulation frequency, intensity, pulse width, duration, and “geometry”,determined by the location of stimulating contacts, their orientation,and their polarity in reference to the epileptogenic zone (FIG. 12).

Data Processing

Each subject's ECoG was stored on a hard disk together with log filesdocumenting the times of each automated detection, stimulation, andcorresponding stimulation parameters. ECoG's were reanalyzed off-line intheir entirety to ensure uniformity of algorithm detection parametersbetween control and experimental phases. A snapshot of ECoG starting 7.5seconds before and ending 7.5 seconds after the onset of each automatedseizure detection was printed and reviewed visually by an independentexpert, who classified each detection as either a seizure (truepositive), interictal epileptiform discharge(s), or no seizure (falsepositive). Another 15-second snapshot centered at the time of eachstimulation attempt was printed for visual review by an independentexpert to ascertain the presence of a stimulation artifact; if noartifact was seen, the attempt was classified as a failed stimulationand the seizure was classified as non-stimulated.

Statistical Analysis

Principal component analysis (PCA) is a standard multivariate method[11] for determining the relative contributions of one or more factorsto the overall variability observed in a set of measurements (e.g.,seizure intensity, duration, etc.). PCA finds linear combinations suchthat the first linear combination (principal component 1; PC1) accountsfor the largest variation in the original data set, the second linearcombination (PC2) accounts for as much of the remaining variation in thedata as possible subject to the condition that it is uncorrelated withPC1, the third linear combination (PC3) accounts for as much of theremaining variation in the data as possible while remaining uncorrelatedwith both PC1 and PC2, and so on. Simply put, PCA identifies theindependent/perpendicular directions in which the data varies, andorders these directions by variance from the largest to the smallest. Bylimiting subsequent analysis to the main principal componentsresponsible for the bulk of data variation and by ignoring the remaining(essentially unused) degrees of freedom in the data, this approachoffers an efficient and effective way to control the trade-off betweenlosing information and simplifying analysis of the matter at hand.

For this study the dependent variables or measurements of interest foreach seizure are: 1. Intensity; 2. Duration; 3. Extent of spread (numberof electrodes contacts registering seizure activity), and 4. Time (inseconds) elapsed between the end of a seizure and the onset of the nextone. A separate principal component analysis (PCA) was carried out oneach of the eight subjects (Local Closed-Loop 1-4 and Remote Closed-Loop1-4). As originally measured, the values of intensity, duration, extentof spread and time between seizures were not normally distributed (FIG.11) and were thus transformed using natural logarithms (Ln) to“normalize” them and make them more suitable for PCA. The time betweenseizures for the last recorded seizure in a trial and also during gapsin the recordings (e.g., when subjects were disconnected for imagingstudies) is not defined and could not be included in the PCA analysis.

Regression analysis of the principal component measures which representcombinations of intensity, duration, spread and time between seizures,was carried out separately for each subject to quantify the effects, ifany, of the following factors or independent variables: (1) Delivery ofelectrical currents (stimulation) to the seizure under consideration orto previous ones; (2) Time elapsed since the last stimulation; (3)Number of seizures since the last stimulation; and (4) Time of day thata seizure occurred [4, 13].

More specifically, the following factors are considered in theregression models:

1. The time of day (TDay), divided into 6 hour periods: midnight-6 am, 6am-noon, noon-6 pm and 6 pm-midnight. TDay reflects circadian variation.

2. Study phase (SP): Control phase (CP) which is assigned a value of 0vs. experimental phase (EP) which is assigned a value of 1.

3. Stimulation (Stim): A value of 0 was assigned to a non-stimulatedseizure and a value of 1 to a seizure that was stimulated.

4. Successive stimulations (SStim). In a few instances where thestimulation schedule was incorrectly programmed or unstimulateddetections turned out to be false positive, successive seizures weresometimes stimulated instead of every other seizure. If the seizureimmediately before the one being presently considered was stimulated,SStim was assigned a value of 1; and if not, the assigned value was 0.

5. Previous stimulations (PStim) are taken into account with thisindicator which is 0 if no seizures prior to the one being consideredwere stimulated, or 1 if any, including the immediately previousseizure, were stimulated. Note that PStim encompasses SStim.

6. Time Post-Stimulation (TPS): Corresponds to the time elapsed sincethe last stimulation (in hours) and was assigned a value of 0 if nostimulations occurred before the seizure presently considered. Thenumber of seizures since the last stimulation was also considered as avariable, but discarded since it did not provide additional usefulinformation.

By way of example, if the only stimulated seizure SZ(Stim) was thatwhich precedes the one presently under consideration—an example sequencewould be SZ(No Stim), SZ(Stim), SZ—then SStim=1, PStim=1 and TPS is thetime in hours since the last stimulated seizure. If previous seizuresbut not the one immediately before the SZ presently under considerationwere stimulated—e.g., SZ(Stim), SZ(Stim), SZ(No Stim), SZ—then, SStim=0,PStim=1, and TPS=time in hours since the last stimulated seizure. For aseizure with no previously stimulated seizures, only the time of day andstimulation are included in the regression model since SStim, PStim andTPS are all zero.

7. The Stimulation Parameter Configuration (SPC) used. For example, forsubject LCL-1 there were five configurations labeled 1 for nostimulation; 2 for the first stimulation configuration; 3 for thesecond; 4 for the third; and 5 for the fourth, respectively.

8. Prior Stimulation Parameter Configurations (PSPC) in the case ofsuccessive (SStim) or previous stimulations (PStim).

Model Terms in the Model A1 Time of Day (TDay) A2 TDay + Study Phase(SP) A3 TDay + SP + Stimulation (Stim) A4 TDay + SP + Stim + SuccessiveStimulations (SStim) A5 TDay + SP + Stim +SStim + Previous Stimulations(PStim) + Poststimulation time (TPS) B3 TDay + SP + StimulationParameter Configuration (SPC) B4 TDay + SP + SPC + Previous StimulationParameter Configurations (PSPC) B5 TDay + SP + SPC + PSPC + PStim + TPS

TABLE 2 % Stimulation Subject SZ Stims. SPC Variable Var Sig. Corr.Terms in Model Effect LCL-1 367 25 4 SS 0.8  0.046 0.14 Stim BeneficialTBS 12.1 <0.001 None TDay None LCL-2 84 4 1 SS 25.2 <0.001 None TDay +Stim + SStim1 + TPS Beneficial & Detrimental TBS None None None NoneNone LCL-3 682 18 3 SS 14.5 <0.001 0.29 TDay + SStim + SPC DetrimentalTBS None None None None None LCL-4 81 2 1 SS None None None None NoneTBS 8.3  0.005 0.28 SStim1 Beneficial RCL-1 197 33 4 SS 16.1 <0.001 0.23TDay + Stim + SStim + PStim Beneficial & Detrimental TBS 33.1 <0.0010.76 TDay + PSPC + PStim + TPS Beneficial & Detrimental RCL-2 3462 53710 SS 10.1 <0.001 0.32 TDay + EP + SPC + PSPC + Beneficial & PStim + TPSDetrimental TBS 2.2 <0.001 0.20 EP + SPC + PStim + TPS Beneficial &Detrimental RCL-3 159 2 1 SS 17.7 <0.001 0.32 TDay + EP Not significantTBS 19.9 <0.001 0.45 TDay + EP Beneficial RCL-4 216 25 3 SS 3.7  0.0030.30 Stim Beneficial TBS 11.5 <0.001 0.35 EP + PSPC Beneficial &Detrimental Table 2. Models and factors considered for estimation ofseizure severity and time between seizures (TBS).

Table 2 shows the regression models systematically and incrementallytested for the contribution of potentially relevant independentvariables (e.g., time of day, stimulation vs. no stimulation of theseizure under consideration and/or stimulation of the seizureimmediately before the one under consideration, etc) to changes (if any)in the value of a dependent variable (e.g., seizure duration). Forexample, the model: TDay+SP+Stim gives a regression equation for avariable Y as follows:

Y=a+b ₂ TDay₂ +b ₃ TDay₃ +b ₄ TDay₄ +c SP+d Stim,

where a, b₂, b₃, b₄, c and d are regression coefficients to beestimated, TDay_(i) is equal to 1 for the period of day being considered(e.g., midnight-6 am) or is otherwise 0, study phase is 0 for thecontrol and 1 for the experimental phase, and Stim is 0 for nostimulation and 1 for stimulation. This approach assesses not only theeffect of the therapy, but of other factors that may confound itseffects, as will be illustrated particularly with respect to thecircadian impact on seizure intensity.

The complexity of the regression models increases from model a1 to a5through the addition of factors (e.g., Stim), culminating in model a5that incorporates all factors mentioned above. Models b3 to b5 aresimilar to models a3-a5, but take into account the different sets ofstimulation parameter configurations used and are applicable only tothose subjects for which different configurations were tested. Once allof the models a1-a5 and b3-b5 (if applicable) are estimated, either a5or b5 is selected, depending on which gives the most significantregression equation.

The regression residuals from a fitted model are the differences betweenthe observed values of the dependent variable (e.g., intensity) and thevalues predicted by the regression equation. Data collected repeatedlyover time, such as seizures, is prone to be serially correlated or toshow persistence. This means that if the intensity of the presentseizure is high, the intensity of the seizure that follows it is likelyto high as well. For some subjects the regression residuals displaypositive serial correlation, which means that there is a tendency forthe residuals from successive seizures to be similar. For example, theremay be several seizures with observed values considerably higher thanexpected. If serial correlation in regression residuals [12] is detectedin any of the final models considered here, it is taken into accountusing the option in GenStat [16] to estimate both a regression model andthe serial correlation between successive residuals using the principleof maximum likelihood.

To properly interpret the results, the following should beconsidered: 1. When comparing regression models, the one which accountsfor the highest percentage of variability in the data is considered thebest; 2. The significance of a regression equation is the probability ofgetting a fit that is just as good as the equation provides simply bychance, that is, in the absence of a relationship between the dependentvariable and those variables to which it is being related. In this sensethe most significant regression equations (with the smallest p values)are better than those with larger p values.

Results

The first principal component (PC1) (Table 3) corresponds to a weightedsum of the standardized values of the natural logarithm of seizureintensity, duration and extent of spread and accounts for the majority(52-63%) of the variation in the data. Interestingly, intensity,duration and extent of spread always have approximately the samecoefficient values and contribute in similar amounts to PC1. Given thisresult, these variables may be conflated into one: Seizure Severity.Simply stated and making allowances for lack of statistical rigor, thefirst principal component encompasses 3 of 4 possible seizurevariables/dimensions.

The second principal component (PC2) (Table 3) represents mainly thetime between the end of a seizure and the onset of the next one, andaccounts for 25% of the variation in the data.

TABLE 3 Table 3. Results of principal components analyses for each ofthe 8 subjects (LCL 1-4; RCL 1-4, showing for each subject the number ofseizure clusters analyzed (Clusters), the coefficients of Ln(Si),Ln(Sd), Ln(Sc) and Ln(ISI) for PC1 to PC4, the variance accounted for byeach principal component, the percentage of the total variation thatthis represents, and the cumulative percentage of variation accountedfor by PC1, PC1 & PC2, PC1 to PC3 and PC1 to PC4 (which is always 100%).Seizures per Hour Change Subject CP EP % LCL1 0.096 0.000 −100.0 LCL20.037 0.015 −59.5 LCL3 0.078 0.106 36.8 LCL4 0.018 0.000 −100.0 RCL10.075 0.021 −72.8 RCL2 0.042 0.045 5.6 RCL3 0.048 0.022 −53.3 RCL4 0.0490.025 −48.6 All 0.052 0.027 −47.2

PC3 and PC4 account for 12-23% of the data variation and werecombinations in different proportions of intensity, duration, spread andthe time between seizures. PC3 and PC4 were excluded from furtheranalysis due to their small contribution to the variation in the data.

For simplicity PC1 or its equivalent seizure severity (SS), was replacedin the regression analyses by the average of the standardized values(mean 0, standard deviation 1) of the natural logarithm of intensity,duration and spread so that SS=1/3(Ln Intensity+Ln Duration+Ln Spread),and PC2 was replaced by the natural logarithm of the time betweenseizures variable. The standardization was done by applying alogarithmic transformation to the values of each then subtracting themean and dividing by the standard deviation in an effort to transformthe raw component data into a distribution approximating standardnormal.

Since the first two principal components are effectively measures ofseizure severity and recurrence rate respectively, results of regressionmodeling and the influence of the various factors considered may beinterpreted accordingly. Table 4 summarizes the regression results foreach subject for whom relevant details are provided below.

Subject LCL-1

The regression analysis was performed on 367 seizures: 290 in thecontrol phase and 77 in the experimental phase (EP). In the EP, a totalof 25 seizures were stimulated with 4 different configurations.

Electrical stimulation (Stim, model a3, Table 2) was the only factorwith significance at the 5% level, reducing mean seizure severity by0.359. Allowing for the existence of serial correlations [the Pearsoncorrelation coefficient between one residual and the next is positive(+0.13)] the estimated reduction in severity was 0.393, with theestimated correlation between one regression residual and the next being0.135, which is significant at the 1% level. No significant differenceswere observed between the four stimulation configurations used. The timebetween seizures was not significantly influenced by stimulation or anyof the treatment factors considered in the analyses. Time of day effectson time between seizures were positive and significant (p=0.02) for Gamto noon, noon to 6 pm and 6 pm to midnight, indicating that time betweenseizures is longer during these periods than from midnight to 6 am.

Subject LCL-2

The regression analysis was performed on 84 seizures: 51 in the controland 33 in the experimental phase (EP). In the EP stimulation wasattempted for 25 seizures, but due to failure of a stimulator's battery,only 4 were actually treated, all with the same configuration, makingmodels b3-b5 superfluous.

Stimulation reduced mean seizure severity by 1.854; the effect ofstimulations following the first one is estimated to be=−2.373+0.335(TPS), where TPS is the time elapsed from the previous stimulation. Bycontrast, for a non-stimulated seizure (during the experimental phase)the effect is estimated to be −0.519+0.335 (TPS). That is, contingentstimulation has the effect of immediately reducing seizure severity, aneffect that outlasts the duration of stimulation (“beneficialcarry-over” effect). However, the beneficial “carry-over” effect notonly decays over time, but at some point “reverses” direction, becomingdetrimental. Indeed, the last seizure in the experimental phase, whichoccurred 11.55 hours after the last stimulation, was the most severe ofthis phase; the estimated detrimental “carry-over” effect on it was−0.510+0.335×11.55=+3.36, suggestive of a “rebound” effect.

Time of day also impacted severity: Mean seizure severity issignificantly higher from noon to 6 pm than from midnight to 6 am, andhigher from 6 pm to midnight than from midnight to 6 am, but notsignificantly higher. The mean severity was similar for midnight to 6 amand 6 am to noon.

None of the factors considered in the regression model had a significanteffect on the time between seizures variable.

Subject LCL-3

Six hundred and eighty two seizures were analyzed, 539 in the controland 143 in the experimental phase. Eighteen seizures were stimulatedwith three different configurations.

The best fitting model for seizure severity (TDay, PStim, and Unique)accounts for 14.5% of the variation in the data. The estimated serialcorrelation coefficient of 0.294 is very highly significant, providingevidence that the regression residuals are correlated. The model wastherefore estimated with an allowance for serial correlation. All threestimulation parameter configurations are estimated to increase meanseizure severity by 0.7-1.7, with highly significant effects for thefirst two configurations (p≤0.001). Stimulation of the previous seizureincreased mean severity of the following seizure by 0.602 for allconfigurations (p=0.001).

The estimated increases in mean seizure severity corresponded toseizures originating in the secondary epileptogenic zones (frontallobes), whereas the severity of those of mesial temporal origin (theprimary zones), were decreased.

The estimates of the time of day effects indicate that mean seizureseverity was lowest (by about 0.30, which is highly significant) from 6AM to noon and noon to 6 PM than at the other times.

The regression analysis was also carried out on the time betweenseizures variable but no effects were significant at the 5% level.

Subject LCL-4

Eighty one seizures were suitable for analysis with 62 of these in thecontrol and 19 in the experimental phase (EP). Two seizures werestimulated in the EP with one stimulation configuration.

Electrical stimulation had no effect on severity but significantlyreduced the mean of the time between seizure variable, that is, seizurefrequency increased. The estimated mean of the natural logarithm of timebetween seizures before any stimulation was delivered was 8.4 vs. 1.6after the first stimulation. No other effects were significant.

Subject RCL-1

Analysis was performed on 197 seizures with 61 in the control and 110 inthe experimental phase (EP). Thirty three seizures were stimulated inthe EP with four different configurations.

Allowing for significant serial correlation stimulation increased themean severity by 0.432 (p=0.013) and if the previous seizure was alsostimulated the intensity of the one presently being treated furtherincreased by 0.469 (p=0.008). However, the severity of a non-stimulatedseizure following one that was stimulated was estimated to be reduced by1.246 (p<0.001). These findings illustrate the presence of an immediateparadoxical (worsening) cumulative effect of stimulation and a ofbeneficial post-stimulation (carry-over) effect.

Allowing for serial correlation which is very significant (r=0.77,p<0.001) the effect of stimulating the previous seizure usingconfiguration 18 significantly (p=0.019) increased the (log) timebetween seizures by 3.03. Also the effect of the time since the laststimulation is significant (p=0.033). However, it is estimated thatafter the first stimulated seizure, regardless of the stimulationconfiguration (including 18) the mean of the time between seizures dropsslightly by 0.087 and then drops by 0.040 for every hour following thelast stimulation.

Subject RCL-2

Analysis was carried out on 3462 seizures with 1650 in the control and1812 in the experimental phase (EP). Electrical currents were deliveredto 537 seizures using 10 different stimulation configurations.

The mean severity of all seizures (stimulated and non-stimulated) waslower (by −0.26, p<0.001) in the experimental phase than for seizures inthe control phase. There were ten stimulation parameter configurationsand their effects on the severity of the seizure under consideration oron the previous seizure are multifarious. Allowing for the presence ofserial correlation some effects are significant (p≤0.05): a) Seizureseverity is reduced by 0.298 by configuration 15 and by 0.270 byconfiguration 16; b) Stimulation of the previous seizure withconfiguration 15 significantly (p=0.008) reduces (by 0.349), theseverity of the seizure under consideration; c) Configuration 10increases seizure severity by 0.571, but stimulation of any seizurebefore the one under consideration with any configuration reducesseverity of present ones by an estimated 0.199 (p=0.010).

The effect of the time elapsed since the previous stimulation onseverity is not significant.

All of the fitted models for time between seizures account for a highlysignificant amount of its variation (p<0.001). However, the model thatallows for different stimulation configurations to have differenteffects was chosen as it fits the data significantly better (p=0.005)than the models that do not allow for differences among configurations.Allowing for highly significant serial correlation (r=0.20, p<0.001),the estimated mean time between seizures is highly significantly(p<0.001) lower (by 0.212) in the experimental than in the controlphase. However, stimulation parameter configuration 15 significantly(p<0.001) increased (by 0.446) the mean time between a seizure and thenext one. In addition after the first seizure was stimulated with anyconfiguration the mean of the time between seizures was estimated tohave increased by 0.073 with a further hourly increase of 0.104.Allowing for the presence of significant serial correlation (r=0.319,p<0.001), seizure severity is significantly (p=0.034) lower from 6 am tomidday than from midnight to 6 am, about the same from midday to 6 pm asfrom midnight to 6 am, and significantly higher from 6 pm to midnightthan from midnight to 6 am (p=0.032).

Subject RCL-3

One hundred and fifty nine seizures (92 in the control and 67 in theexperimental phase (EP)) were analyzed. Only two seizures were treatedin the EP with one stimulation configuration.

All of the fitted models for seizure severity account for a highlysignificant part of the variation in the data but only the effects ofthe time of day and the experimental phase are significant at the 5%level. The final model chosen therefore only includes these effects.Since only one stimulation configuration was used, models allowing fordifferent configuration effects are not considered.

Stimulation significantly (p=0.033) increases seizure severity, but ifallowance is made for the presence of significant serial correlation(p<0.001) the effect becomes non-significant. With an allowance forserial correlation, seizure severity is highly significant (p<0.001)lower from 6 pm to midnight compared to midnight to 6 am.

The mean time between seizures variable was higher by 1.19 (p=0.004) inthe experimental than in the control phase and the estimated timebetween seizures was significantly higher (p<0.01) for noon to 6 pm and6 pm to midnight than from midnight to 6 am.

Subject RCL-4

Analysis was performed on 216 seizures with 158 in the control and 58 inthe experimental phase (EP). Twenty seizures were treated with threeconfigurations.

Allowing for the highly significant serial correlation (r=0.30,p<0.001), stimulation reduced mean seizure severity by 0.58 (p=0.001).

With allowance for the presence of the highly significant serialcorrelation for the time between seizures variable, stimulation of theprevious seizure using configuration 1 significantly (p=0.039) increasedthe mean time between seizures by 1.343. Also, the mean time betweenseizures in the experimental phase was 1.23 higher than in the controlphase (p=0.001).

Table 4 summarizes the results for each subject.

TABLE 4 LCL-1 Coefficients RCL-1 Coefficients 367 Clusters PC1 PC2 PC3PC4 197 Clusters PC1 PC2 PC3 PC4 LnSi 0.60 0.02 −0.22 0.77 LnSi 0.60−0.03 0.23 0.77 LnSd 0.55 −0.03 0.81 −0.20 LnSd 0.58 0.07 0.53 −0.61LnSc 0.58 0.07 −0.54 −0.60 LnSc 0.54 −0.20 −0.79 −0.20 LnISI −0.03 1.000.07 0.03 LnISI 0.08 0.98 −0.20 0.03 Variance 2.35 1.00 0.41 0.24Variance 2.51 1.02 0.35 0.12 % of Total 58.7 25.0 10.3 5.9 % of Total62.8 25.4 8.7 3.1 Cumulative % 58.7 83.8 94.1 100.0 Cumulative % 62.888.2 96.9 100.0 LCL-2 Coefficients RCL-2 Coefficients 84 Clusters PC1PC2 PC3 PC4 3462 Clusters PC1 PC2 PC3 PC4 LnSi 0.58 0.16 −0.51 0.62 LnSi0.59 0.06 0.19 0.79 LnSd 0.60 −0.02 −0.24 −0.76 LnSd 0.56 0.02 −0.79−0.23 LnSc 0.55 −0.11 0.81 0.18 LnSc 0.57 0.13 0.57 −0.57 LnISI −0.020.98 −0.17 0.10 LnISI −0.12 0.99 −0.07 0.03 Variance 2.43 1.03 0.38 0.16Variance 2.32 0.98 0.39 0.30 % of Total 60.7 25.7 9.6 4.0 % of Total58.1 24.6 9.8 7.4 Cumulative % 60.7 86.4 96.0 100.0 Cumulative % 58.182.7 92.6 100.0 LCL-3 Coefficients RCL-3 Coefficients 682 Clusters PC1PC2 PC3 PC4 159 Clusters PC1 PC2 PC3 PC4 LnSi 0.56 0.07 0.73 0.38 LnSi0.60 0.02 0.32 0.74 LnSd 0.60 −0.19 −0.04 −0.78 LnSd 0.59 −0.03 0.46−0.67 LnSc 0.57 0.01 −0.68 0.47 LnSc 0.54 0.12 −0.83 −0.09 LnISI 0.070.98 −0.05 −0.18 LnISI −0.06 0.99 0.10 −0.03 Variance 2.07 1.02 0.550.37 Variance 2.52 1.00 0.35 0.13 % of Total 51.6 25.5 13.7 9.2 % ofTotal 62.9 25.1 8.8 3.3 Cumulative % 51.6 77.1 90.8 100.0 Cumulative %62.9 88.0 96.7 100.0 LCL-4 Coefficients RCL-4 Coefficients 81 ClustersPC1 PC2 PC3 PC4 216 Clusters PC1 PC2 PC3 PC4 LnSi 0.58 0.01 −0.41 0.70LnSi 0.58 0.07 0.45 0.67 LnSd 0.58 −0.01 −0.39 −0.71 LnSd 0.58 −0.050.35 −0.73 LnSc 0.56 −0.15 0.81 0.02 LnSc 0.54 −0.35 −0.76 0.09 LnISI0.09 0.99 0.13 −0.01 LnISI 0.19 0.93 −0.30 −0.06 Variance 2.46 1.00 0.310.23 Variance 2.48 1.02 0.28 0.23 % of Total 61.6 25.0 7.6 5.8 % ofTotal 61.9 25.5 7.0 5.6 Cumulative % 61.6 86.5 94.2 100.0 Cumulative %61.9 87.4 94.4 100.0 Table 4. Summary of the results for each subject(1-8). LCL = Local Closed-Loop; RCL = Remote Closed-Loop; SS = SeizureSeverity; SZ = Total number of seizures; Stims. =: Number of Seizuresstimulated; SPC = Number of stimulation parameter configurations;Variable = Dependent Variable; % Var = Percentage of variation accountedfor by the final model; Sig = Significance (p) of the equation; Corr =Estimated serial correlation; Terms in model = Number of terms in finalmodel; Stimulation Effects = Estimated stimulation effects.

Discussion

Therapeutic decisions in the management of the epilepsies rely onsubjective measures of clinical seizure frequency typically gleaned frompatient diaries, which are notoriously inaccurate [1, 2]. Thislimitation may be unavoidable or even acceptable if anti-seizure drugsare a viable option, but when alternative therapies must be considered,as in the case of pharmaco-resistant epilepsies, accurate quantificationof all relevant seizure dimensions is desirable in order to attempt toincrease efficacy, reduce adverse events and understand theirspatio-temporal behavior over short or long time scales. Consider, forinstance, brain electrical stimulation. As these results demonstrate,clinical seizure frequency alone, even if accurately measured, would beinadequate for assessment of efficacy and selection of “optimal”stimulation parameters. The effect of electrical stimulation, which asshown in this study may be beneficial or detrimental (depending amongother factors on site of delivery and the parameters used), is notrestricted to seizure frequency but also impacts intensity, duration andextent of spread or severity. Thus, valid assessment and optimization ofany anti-seizure therapy requires quantitative analyses that includesubclinical—not just clinical—events as well as statistical methods thattake into account the effects of timing of stimulation and of its otherparameters, circadian influences (time of day), the presence or absenceof immediate and prolonged (“carry-over”) stimulation effects and oftheir “valence” (positive or negative) and the time elapsed since thedelivery of the last treatment.

The results of the analyses performed on these subjects, bring to theforefront the following observations:

1. While it is obvious that seizures can be characterized by theirintensity, duration, extent of spread and rate of occurrence, thesevariables or dimensions have not been considered in investigations ofthe dynamics of epilepsy or in the assessment of therapies, due untilrecently to the lack of means for quantifying said dimensions.Worthwhile characterization of seizure dynamics and assessment ofefficacy of therapies requires that all four dimensions be considered.Intensity, duration and extent of spread may be conflated into a singleclinically useful measure, seizure severity (SS) [6], that as shown bythe PCA analysis may be conveniently represented by the standardizedaverage of their natural logarithms [SS=1/3 (LnSi+LnSd+LnSc)];interestingly this measure is identical to the logarithm of thegeometric mean. Similarly, measures of seizure frequency are likely toyield valuable insight into the temporal behavior of the epilepsies. Aframework that includes both measures, severity and frequency, willtranslate into a more valid assessment of therapeutic efficacy and inthe case of electrical stimulation, allow probing of the extent to whichdelivery of currents to the brain—and seizures themselves—affect thetiming and likelihood of occurrence of future seizures. As shown hereand in previous work [4], measures of seizure severity areinter-correlated: the more intense and longer the seizure, the higherthe probability of spread and generalization. Intercorrelation betweenmeasures of severity is a source of feature redundancy, that is, eachmeasure is dependent not only on extraneous factors but also on theother measures in the set, a property that must be accounted for in thestatistical analysis of any data to avoid errors in hypothesis testing.

2. That intensity, duration, spread and time between seizures may not benormally distributed (FIG. 11) must be taken into account in theselection of tests for statistical analyses. Furthermore, the influenceof extraneous factors (circadian rhythm, drug taper, sleep stage, etc.)on seizure severity and recurrence may manifest itself in the form ofserial correlation in seizure severity and recurrence [7, 8].

3. The multifarious nature of the effects of electrical stimulation, asperformed in this trial, at both the intra- and inter-individual levels.Seizure severity and time between seizures, as measured here wassignificantly reduced in certain subjects, increased in others andunchanged in some. Moreover, not only were beneficial effects inseverity not necessarily extensive to time elapsed between seizures, butdual effects (beneficial and detrimental) were observed in the samesubject for seizure severity (Subjects LCL-2, RCL-1, 2) and for timeelapsed between seizures (Subjects RCL-1, 2, 4). The effects of changesin stimulation parameters were complex and may be attributable in partto difference in brain excitability as a function of time. In subjectRCL-2, one parameter configuration (143 Hz., 5.5 mA; 30 s; 100 us)increased seizure severity while other configurations (143 Hz., 8.5 mA;30 s; 100 us and 200 Hz.; 5 mA; 30 s; 100 us) had the opposite effect.

4. The existence of effects on severity and length of time betweenseizures that are not only immediate but also outlast the passage ofelectrical currents into nervous tissue (“carry-over” effect), and thatmay be beneficial and/or detrimental intra- and inter-individually. Itwas noted that in certain subjects (LCL-2), an immediate beneficialeffect on severity or frequency, may be followed by a delayeddeleterious one (“rebound” phenomenon) in the same subject with the samestimulation parameters. These findings suggest that the effects ofelectrical stimulation on seizure severity and frequency in certainsubjects may be that of a “trade-off”, that is, reduction in onevariable may result in an increase in the other, so the net effect evenif beneficial may be difficult to detect using conventional statisticalanalysis tools. This underscores the importance of quantification of allseizure dimensions to fully characterize the effects of any therapy.

These results are in line with those previously reported (Table 5) [6];differences between them are not substantive as they merely reflect thepooling of clinical and so-called sub-clinical seizures (without overtbehavioral changes) which were the majority. Comparison of that study(whose analysis focused on clinical seizures) with this one motivates aplausible observation: while modest in magnitude, the reductions in meanseverity may have been sufficient to prevent the evolution ofsub-clinical into clinical seizures. Conventional analysis of theseresults would have only revealed that mean clinical seizure frequencydecreased by 72% in 6/8 subject and increased by 21.2% in 2/8, possiblydue to contingent electrical stimulation, observations that are accuratebut incomplete and not enlightening. This study's analyses identifiedand quantified the contributions of the independent variables(stimulation, time of day, etc.) to the measured changes in thedependent variables (severity and frequency); as an example, theincrease in seizure severity in one case (RCL3) was unrelated tostimulation; it also uncovered a detrimental “carry-over” effect andconfirmed the existence of a beneficial immediate and prolonged(“carry-over”) stimulation effect that had been hypothesized in aprevious publication [6].

TABLE 5 Config. f I PW (us/ Location No. of Subject No. (Hz) (mA) Dur(s) phase) code clusters LCL-1 1 NO STIMULATION 344 4 50 10 1 200 2 5 5333 10 1 200 1 4 6 333 10 1 200 2 10 7 500 10 1 200 2 7 LCL-2 1 NOSTIMULATION 81 6 125 5 1 100 3 4 LCL-3 1 NO STIMULATION 666 2 100 5 1200 1 3 3 125 5 1 200 1 11 4 225 5 1 200 1 4 LCL-4 1 NO STIMULATION 83 7100 5 1 100 3 2 RCL-1 1 NO STIMULATION 169 13 100 1.5 30 100 5 27 18142.9 1.5 30 100 5 1 19 142.9 2.5 30 100 5 2 RCL-2 1 NO STIMULATION 29297 100 5 2.5 100 3 61 8 142.9 5 2.5 100 3 92 9 142.9 5 30 100 3 118 10142.9 5.5 30 100 3 14 11 142.9 6 30 100 3 6 12 142.9 6.5 30 100 3 3 13142.9 7 30 100 3 2 14 142.9 8 30 100 3 1 15 142.9 8.5 30 100 3 46 16 2005 30 100 3 194 RCL-3 1 NO STIMULATION 168 12 100 5 2.5 100 6 2 RCL-4 1NO STIMULATION 193 4 100 5 30 100 2 5 5 142.9 5 30 100 2 10 7 200 5 30100 2 10 Table 5. Number of clinical seizures/hour for each subject. Sixof the eight subjects had a reduction in seizure frequency in theExperimental Phase (EP) compared to the Control Phase (CP). For allsubjects pooled seizure frequency is reduced by 47.2%, from 0.052seizures/hour to 0.027 seizures/hour.

Two inferences, at once promising and sobering may be drawn from thisstudy: one, that contingent electrical stimulation deserves a place inthe armamentarium of therapies for pharmaco-resistant seizures and theother that its apparently narrow therapeutic ratio calls for carefulimplementation and real-time quantification of its effects onepileptogenic brain tissue. Electrical currents may be delivered notonly contingently for the purpose of abating seizures but also“prophylactically” when the beneficial “carry-over” effect (if present)begins to wane; in subjects such as LCL-2 in whom the beneficial“carry-over” gives way to a detrimental effect, analyses of ECoGrecordings over a suitable time interval may allow identification of thetime at which the therapeutic effect changes “direction”, a necessarystep for attempting to revert this trend.

While electrical stimulation was the treatment modality investigated inthis study, the proposed approach and methods are applicable to anyother therapeutic modality. Dose, coefficient of diffusion and type ofdrug and temperature and diffusivity for a thermal modality, wouldreplace stimulation parameters in the assessment of efficacy. Advancesin the understanding of the mechanisms of action of anti-seizuretherapies are unlikely to materialize unless measures of intensity,duration, extent of spread and time between seizures are adopted andanalyzed with suitable (e.g., multivariate) statistical tools.

REFERENCES

All the references listed below are hereby incorporated by referencehere to the extent they provide relevant information.

-   1. Blum D E, Eskola J, Bortz J J, Fisher R S. Patient awareness of    seizures; Neurology 2000; 47:260-4-   2. Hoppe C, Poepel A, Elger C E. Epilepsy: accuracy of patient    seizure counts. Arch Neurol 2007; 64, 1595-99.-   3. Osorio I, Frei M G, Wilkinson S B. Real-time automated detection    and quantitative analysis of seizures and short-term prediction of    clinical onset. Epilepsia; 1998; 39, 615-27.-   4. Osorio I, Frei M G, Manly B F J, Sunderam S. An introduction to    contingent (closed-loop) brain electrical stimulation for seizure    blockage, to ultra-short-term clinical trials and to    multidimensional statistical analysis of therapeutic efficacy. J    Clin Neurophysiol 2001; 18:533-544.-   5. Osorio I, Frei M G, Giftakis J, Peters T. Performance    re-assessment of a real-time seizure detection algorithm on long    ECoG series. Epilepsia 2002; 43:1522-35.-   6. Osorio I, Frei M, Sunderam S, Bhavaraju N. Automated Seizure    Abatement in Humans Using Electrical Stimulation. Ann Neurol 2005;    57:258-68-   7. Lasemidis, L D, Olson L D, Savit R S, Sackellares, J C. Time    dependencies in the occurrences of epileptic seizures; Epilepsy Res    1994; 17:81-94.-   8. Sunderam S, Osorio I, Frei M G. Epileptic seizures are temporally    interdependent under certain conditions. Epilepsy Res., 2007;    76:77-84.-   9. Barkley G L, Smith B, Bergey G, Worrell G, Drazkowski J, Labar D,    Duchrow R, Murro A, Smith M, Gwinn R, Fish B, Hirsch L, Morrell M.    Safety and preliminary efficacy of a responsive neurostimulator;    Neurology 2006 (Supp. 2) A387.-   10. Fisher R S, Salanova V, Witt T, Henry T, et al. Electrical    Stimulation of Anterior Nucleus of Thalamus for Treatment of    Refractory Epilepsy; Epilepsies In Press.-   11. Manly, B F J. Multivariate Statistical Methods: a Primer, 3rd    Edition; Boca Raton Chapman and Hall/CRC, 2004.-   12. Manly, B F J. Statistics for Environmental Science and    Management, 2nd Edition; Boca Raton Chapman and Hall/CRC 2008.-   13. Durazzo T S, Spencer S S, Duckrow R B, Novotny E J, Spencer D D,    Zaveri H P. Temporal distributions of seizure occurrence from    various epileptogenic regions. Neurology 2008; 70:1265-71-   14. Peters T E, Bhavaraju N C, Frei M G, Osorio I. Network system    seizure detection and contingent delivery of therapy; J Clin    Neurophysiol 2001; 18:545-549.-   15. Agnew W F, McCreery D B, eds. Neural prostheses: fundamental    studies. Englewood Cliff, N.J.: Prentice Hall, 1990.-   16. Lawes Agricultural Trust (2008). GenStat 11. VSN International,    U.K.

All of the methods and systems disclosed and claimed herein can be madeand executed without undue experimentation in light of the presentdisclosure. While the compositions and methods of this disclosure havebeen described in terms of particular embodiments, it will be apparentthat variations may be applied to the methods and systems and in thesteps or in the sequence of steps of the method described herein withoutdeparting from the concept, spirit and scope of the disclosure. All suchsimilar substitutes and modifications apparent to those skilled in theart are deemed to be within the spirit, scope and concept of thedisclosure as defined by the appended claims.

REFERENCES

The following references, to the extent that they provide exemplaryprocedural or other details supplementary to those set forth herein, arespecifically incorporated herein by reference.

-   U.S. patent application Ser. No. 12/756,065, filed Apr. 7, 2010-   U.S. patent application Ser. No. 12/770,562, filed Apr. 29, 2010-   U.S. patent application Ser. No. 12/896,525, filed Oct. 1, 2010-   U.S. patent application Ser. No. 13/040,996, filed Mar. 4, 2011-   U.S. patent application Ser. No. 13/091,033, filed Apr. 20, 2011-   U.S. patent application Ser. No. 13/098,262, filed Apr. 29, 2011-   U.S. Pat. No. 4,702,254-   U.S. Pat. No. 4,867,164-   U.S. Pat. No. 5,025,807-   U.S. Pat. No. 6,961,618-   U.S. Pat. No. 7,457,665

What is claimed:
 1. A method of assessing an efficacy of an epilepsytherapy via one or more processors of one or more medical devices, themethod comprising: assessing the efficacy of the epilepsy therapy;selecting a plurality of dependent variables relating to each of aplurality of seizures in a patient; selecting a plurality of independentvariables, wherein each independent variable comprises a therapyparameter, a therapy delivery parameter, a temporal factor, anenvironmental factor, or a patient factor; quantifying at least onerelationship between each of at least two dependent variables and eachof at least two independent variables; and performing an action inresponse to the quantifying, selected from reporting the at least onerelationship, reporting the efficacy assessment of the epilepsy therapy,assessing an adverse effect of the epilepsy therapy, providing a therapymodification recommendation, or adjusting the epilepsy therapy.
 2. Themethod of claim 1, further comprising ranking at least two of theplurality of the independent variables, at least two of the plurality ofthe dependent variables, or both, according to at least one of amagnitude or a direction.
 3. The method of claim 1, further comprisinglogging to memory data relating to the at least one relationship.
 4. Themethod of claim 1, wherein each of the dependent variables is selectedfrom an intensity of a seizure, a duration of the seizure, an extent ofspread of the seizure, a seizure severity index, a seizure frequency, aninter-seizure interval(s), or the adverse effect of the epilepsytherapy.
 5. The method of claim 4, wherein at least one of the pluralityof dependent variables is the seizure severity index, wherein theseizure severity index comprises one or more of an autonomic index, aneurologic index, a metabolic index, a respiratory index, a tissuestress marker, a musculoskeletal index, or an endocrine index.
 6. Themethod of claim 1, wherein each of the plurality of independentvariables is selected from whether a seizure was treated with theepilepsy therapy, whether a prior seizure was treated with the epilepsytherapy, a time elapsed since a previous treatment with the epilepsytherapy, a number of seizures since a previous treatment with theepilepsy therapy, a time of day the seizure occurred, a time of monththe seizure occurred, a time of year the seizure occurred, a type of theepilepsy therapy, a dose of the epilepsy therapy, a current density, adegree of tissue cooling, a degree of tissue warming, a level ofconsciousness, a level and type of cognitive activity, a level and typeof physical activity, a state of health, a concentration of medicamentsor chemicals in a tissue, or two or more of the foregoing.
 7. The methodof claim 1, wherein the epilepsy therapy is selected from an electricalstimulation of a target structure of a brain, an electrical stimulationof a cranial nerve, a drug, a thermal treatment of a neural structure, acognitive therapy, or two or more thereof.
 8. The method of claim 1,wherein the quantifying comprises a regression analysis on the pluralityof the dependent variables and the plurality of the independentvariables.
 9. A medical device system, comprising: a data acquisitionunit configured to acquire data relating to a plurality of dependentvariables relating to each of a plurality of seizures and a therapy anddata relating to a plurality of independent variables, wherein each ofthe independent variables comprises a therapy parameter, a therapydelivery parameter, a temporal factor, an environmental factor, or apatient factor; a data quantification unit configured to quantify atleast one relationship between at least two of the dependent variablesand at least two of the independent variables; and a responsive actionunit configured to perform an action in response to the quantifying,wherein the action is selected from reporting the at least onerelationship, assessing an efficacy of the therapy, assessing an adverseeffect of the therapy, providing a therapy modification recommendation,or adjusting the therapy.
 10. The medical device system of claim 9,wherein the data quantification unit comprises a regression analysisunit configured to perform a regression analysis on the at least two thedependent variables and the at least two the independent variables. 11.The medical device system of claim 9, further comprising at least onesensor configured to sense at least one body signal relating to at leastone of the plurality of dependent variables, and wherein the dataacquisition unit is configured to acquire at least some dependentvariable data based on the at least one body signal.
 12. The medicaldevice system of claim 9, further comprising at least one sensorconfigured to sense at least one signal relating to at least one of theplurality of independent variables, and wherein the data acquisitionunit is configured to acquire at least some independent variable databased on the at least one signal.
 13. The medical device system of claim9, further comprising a therapy unit configured to administer at leastone therapy to the patient.
 14. The medical device system of claim 13,wherein the therapy unit is configured to: administer an electricalstimulation to a target structure of a brain, a target portion of acranial nerve, or both of the patient; a drug to a body of a patient; athermal therapy to a target structure of the brain, a target portion ofthe cranial nerve, or both of the patient; a sensory therapy to asensory organ of the patient; or a cognitive therapy to the patient. 15.A method implemented via one or more processors of one or more medicaldevices, the method comprising: receiving data relating to a pluralityof independent variables selected from therapy parameters, patientparameters, or temporal parameters; receiving data relating to aplurality of dependent variables resulting from each of a plurality ofepileptic events undergone by a patient; modeling a relationship betweenthe plurality of the independent variables and the plurality of thedependent variables using a model; ranking at least two independentvariables based upon the modeling, wherein the ranking according to atleast one of a magnitude and a direction of the relationship; andinitiating one or more therapeutic actions based on the ranking.
 16. Themethod of claim 15, further comprising performing a model adjustmentprocess based upon the modeling, wherein the model adjustment processcomprises determining whether the model has a predetermined goodness offit, and adjusting the model is based at least in part on a finding thatthe model lacks the goodness of fit.
 17. The method of claim 16, whereinadjusting the model comprises adding or removing at least oneindependent variable from the plurality of independent variables. 18.The method of claim 16, further comprising assessing an efficacy of themodel.
 19. The method of claim 16, further comprising assessing anefficacy of the one or more therapeutic actions.
 20. The method of claim19, further comprising modifying the one or more therapeutic actionsbased on the efficacy assessment of the one or more therapeutic actions.